What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Database
- Programming language
His main research concerns Information retrieval, Search engine, Search analytics, Metasearch engine and Web search query.
In general Information retrieval study, his work on Query expansion often relates to the realm of Term, thereby connecting several areas of interest.
The concepts of his Query expansion study are interwoven with issues in Query language, Query by Example, Ranking and Query optimization.
Weiyi Meng is involved in the study of Search engine that focuses on Search-oriented architecture in particular.
His Metasearch engine study incorporates themes from Database and Information needs.
As a part of the same scientific study, Weiyi Meng usually deals with the Web search query, concentrating on Semantic search and frequently concerns with User profile, Personalization, User modeling, Set and Interface.
His most cited work include:
- Personalized Web search for improving retrieval effectiveness (386 citations)
- Building efficient and effective metasearch engines (374 citations)
- Effective keyword search in relational databases (329 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Information retrieval, Search engine, Metasearch engine, Web search query and Data mining.
His research investigates the connection with Information retrieval and areas like Database which intersect with concerns in Metadata.
His work carried out in the field of Search engine brings together such families of science as The Internet and Cluster analysis.
His Metasearch engine research integrates issues from Deep Web, Local search and Set.
His study in the field of Web query classification is also linked to topics like Crawling.
Weiyi Meng has researched Query expansion in several fields, including Ranking and Ranking.
He most often published in these fields:
- Information retrieval (69.57%)
- Search engine (31.68%)
- Metasearch engine (25.47%)
What were the highlights of his more recent work (between 2014-2020)?
- Information retrieval (69.57%)
- Tuple (7.45%)
- Data mining (21.12%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Information retrieval, Tuple, Data mining, Social media and Relational database.
His research ties Data integration and Information retrieval together.
His Data mining research is multidisciplinary, incorporating elements of Middleware, Distributed database, Ranking and Data set.
His work deals with themes such as Generative model and Service, which intersect with Social media.
His Relational database study integrates concerns from other disciplines, such as Discrete mathematics and Hyperrectangle, Combinatorics.
His Web search query research is included under the broader classification of Search engine.
Between 2014 and 2020, his most popular works were:
- Scaling up copy detection (16 citations)
- Polarity Consistency Checking for Domain Independent Sentiment Dictionaries (11 citations)
- Diversionary Comments under Blog Posts (8 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Database
- Programming language
The scientist’s investigation covers issues in Information retrieval, Sentiment analysis, Theoretical computer science, World Wide Web and Event.
His Information retrieval study combines topics from a wide range of disciplines, such as Spatial contextual awareness, Social media and Pruning.
His Sentiment analysis research includes themes of Domain, WordNet and Word.
His World Wide Web study frequently links to related topics such as Named-entity recognition.
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